A common approach to force the CNN to produce sharper output images is to apply Gaussian smoothing using a fixed standard deviation at training time [1]}, [2]}, [3]}. By training the CNN on blurred low-resolution images, the super-resolution task becomes harder. During inference, when the input images are no longer blurred, the task will be much easier. However, smoothing only the training images with a fixed standard deviation will inherently generate a distribution gap between training and test data. If the CNN fits the training distribution well, it might not produce the desired results at inference time. This is because machine learning models are based on the assumption that training data and test data are sampled from the same distribution. We propose to solve this problem by using a randomly-chosen standard deviation for each training image. Although the training data distribution will still be different from the testing data distribution, it will include the distribution of test samples, as illustrated in Figure REF . In order to augment the training data, we apply Gaussian blur with a probability of \(0.5\)  (only half of the images are smoothed) using a kernel of \(3 \times 3\)  components and a randomly-chosen standard deviation between 0 and \(0.5\) . In this way, we increase the variance of the training data without introducing any bias. In this case, if the CNN fits the training distribution well, it will produce good super-resolution outputs during inference, since there is no distribution gap between training and test.
